Question 18
Domain 2: Explore data and run experimentsYou are analyzing a numerical dataset that contains missing values in several columns. The dataset must be cleaned using an appropriate operation without changing the number of features. The goal is to work with a complete dataset that includes values for all records. The developer chose to replace each missing value using the Multiple Imputation by Chained Equations (MICE) method. Does this solution meet the requirement?
Correct answer: B
Explanation
Yes. Multiple Imputation by Chained Equations (MICE) fills in missing values while preserving the original feature set, so it does not change the number of columns. It produces a complete dataset with values for all records, matching the requirement to clean the data without altering the number of features.
Why each option is right or wrong
A. No. MICE would remove rows with missing values, which changes the dataset size and does not preserve all features.
B. Yes. MICE imputes missing values while keeping the same columns and producing a complete dataset for analysis.
MICE is an imputation procedure under the missing-data framework that estimates each absent entry from the observed values in the existing variables, so it replaces gaps rather than removing rows or adding new features. In this scenario, the dataset remains at the same dimensionality because no columns are created or deleted, and the output is a fully populated table suitable for analysis.